Chen Shao, Chengzhi Fei, Mingxue Gu, Xiujing Zha, Juan Li, Delu Zheng, Diwen Wang, Yanqiu Wang, Xiaolei Hu
{"title":"TyG指数和UHR对2型糖尿病下肢动脉病变的比较预测价值：回顾性分析","authors":"Chen Shao, Chengzhi Fei, Mingxue Gu, Xiujing Zha, Juan Li, Delu Zheng, Diwen Wang, Yanqiu Wang, Xiaolei Hu","doi":"10.2147/DMSO.S496727","DOIUrl":null,"url":null,"abstract":"<p><strong>Objective: </strong>To compare the predictive value of triglyceride glucose index (TyG) and the ratio of serum uric acid (SUA) to high-density lipoprotein cholesterol (HDL-C) (UHR) for lower extremity atherosclerotic disease (LEAD) in type 2 diabetes (T2DM) patients.</p><p><strong>Methods: </strong>303 patients with T2DM were divided into LEAD group (n=192) and non-LEAD group (n=111) based on the results of lower extremity vascular color Doppler ultrasound. All patients were divided into a training set and a validation set at a 7:3 ratio. In the training set, Least absolute shrinkage and selection operator (LASSO) regression was applied to screen for predictive factors of LEAD, and a multivariate logistic regression model was constructed to analyze the predictive factors, with a nomogram being plotted. The discriminative ability and calibration of the model were evaluated using the receiver operating characteristic (ROC) curve area under the curve (AUC) and calibration curves in both the training and validation sets. Decision curve analysis (DCA) was used to evaluate the clinical net benefit.</p><p><strong>Results: </strong>The variables selected by the LASSO regression included age, pulse pressure difference (PP), TyG, and UHR. The multivariate logistic regression model indicated that age, PP, TyG, and UHR were predictive factors for LEAD in T2DM patients (P<0.05). ROC curve analysis suggested that the discriminatory ability was in the following order: the nomogram model (AUC=0.872), TyG (AUC=0.751), and UHR (AUC=0.709), which were greater than that of age and PP. TyG and UHR cut-off values were 9.836 and 216.248, respectively. The specificities of TyG and UHR were 0.760 and 0.547, and the sensitivities were 0.629 and 0.807, respectively. The calibration curve showed the model's predictions matched actual conditions. DCA verified the model's clinical benefit.</p><p><strong>Conclusion: </strong>Both TyG and UHR have good predictive value and are suitable for screening LEAD in T2DM patients.</p>","PeriodicalId":11116,"journal":{"name":"Diabetes, Metabolic Syndrome and Obesity: Targets and Therapy","volume":"18 ","pages":"1341-1351"},"PeriodicalIF":2.8000,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12049115/pdf/","citationCount":"0","resultStr":"{\"title\":\"Comparative Predictive Value of the TyG Index and UHR for Lower Extremity Artery Disease in Type 2 Diabetes: A Retrospective Analysis.\",\"authors\":\"Chen Shao, Chengzhi Fei, Mingxue Gu, Xiujing Zha, Juan Li, Delu Zheng, Diwen Wang, Yanqiu Wang, Xiaolei Hu\",\"doi\":\"10.2147/DMSO.S496727\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Objective: </strong>To compare the predictive value of triglyceride glucose index (TyG) and the ratio of serum uric acid (SUA) to high-density lipoprotein cholesterol (HDL-C) (UHR) for lower extremity atherosclerotic disease (LEAD) in type 2 diabetes (T2DM) patients.</p><p><strong>Methods: </strong>303 patients with T2DM were divided into LEAD group (n=192) and non-LEAD group (n=111) based on the results of lower extremity vascular color Doppler ultrasound. All patients were divided into a training set and a validation set at a 7:3 ratio. In the training set, Least absolute shrinkage and selection operator (LASSO) regression was applied to screen for predictive factors of LEAD, and a multivariate logistic regression model was constructed to analyze the predictive factors, with a nomogram being plotted. The discriminative ability and calibration of the model were evaluated using the receiver operating characteristic (ROC) curve area under the curve (AUC) and calibration curves in both the training and validation sets. Decision curve analysis (DCA) was used to evaluate the clinical net benefit.</p><p><strong>Results: </strong>The variables selected by the LASSO regression included age, pulse pressure difference (PP), TyG, and UHR. The multivariate logistic regression model indicated that age, PP, TyG, and UHR were predictive factors for LEAD in T2DM patients (P<0.05). ROC curve analysis suggested that the discriminatory ability was in the following order: the nomogram model (AUC=0.872), TyG (AUC=0.751), and UHR (AUC=0.709), which were greater than that of age and PP. TyG and UHR cut-off values were 9.836 and 216.248, respectively. The specificities of TyG and UHR were 0.760 and 0.547, and the sensitivities were 0.629 and 0.807, respectively. The calibration curve showed the model's predictions matched actual conditions. DCA verified the model's clinical benefit.</p><p><strong>Conclusion: </strong>Both TyG and UHR have good predictive value and are suitable for screening LEAD in T2DM patients.</p>\",\"PeriodicalId\":11116,\"journal\":{\"name\":\"Diabetes, Metabolic Syndrome and Obesity: Targets and Therapy\",\"volume\":\"18 \",\"pages\":\"1341-1351\"},\"PeriodicalIF\":2.8000,\"publicationDate\":\"2025-04-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12049115/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Diabetes, Metabolic Syndrome and Obesity: Targets and Therapy\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.2147/DMSO.S496727\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2025/1/1 0:00:00\",\"PubModel\":\"eCollection\",\"JCR\":\"Q3\",\"JCRName\":\"ENDOCRINOLOGY & METABOLISM\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Diabetes, Metabolic Syndrome and Obesity: Targets and Therapy","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.2147/DMSO.S496727","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/1/1 0:00:00","PubModel":"eCollection","JCR":"Q3","JCRName":"ENDOCRINOLOGY & METABOLISM","Score":null,"Total":0}
Comparative Predictive Value of the TyG Index and UHR for Lower Extremity Artery Disease in Type 2 Diabetes: A Retrospective Analysis.
Objective: To compare the predictive value of triglyceride glucose index (TyG) and the ratio of serum uric acid (SUA) to high-density lipoprotein cholesterol (HDL-C) (UHR) for lower extremity atherosclerotic disease (LEAD) in type 2 diabetes (T2DM) patients.
Methods: 303 patients with T2DM were divided into LEAD group (n=192) and non-LEAD group (n=111) based on the results of lower extremity vascular color Doppler ultrasound. All patients were divided into a training set and a validation set at a 7:3 ratio. In the training set, Least absolute shrinkage and selection operator (LASSO) regression was applied to screen for predictive factors of LEAD, and a multivariate logistic regression model was constructed to analyze the predictive factors, with a nomogram being plotted. The discriminative ability and calibration of the model were evaluated using the receiver operating characteristic (ROC) curve area under the curve (AUC) and calibration curves in both the training and validation sets. Decision curve analysis (DCA) was used to evaluate the clinical net benefit.
Results: The variables selected by the LASSO regression included age, pulse pressure difference (PP), TyG, and UHR. The multivariate logistic regression model indicated that age, PP, TyG, and UHR were predictive factors for LEAD in T2DM patients (P<0.05). ROC curve analysis suggested that the discriminatory ability was in the following order: the nomogram model (AUC=0.872), TyG (AUC=0.751), and UHR (AUC=0.709), which were greater than that of age and PP. TyG and UHR cut-off values were 9.836 and 216.248, respectively. The specificities of TyG and UHR were 0.760 and 0.547, and the sensitivities were 0.629 and 0.807, respectively. The calibration curve showed the model's predictions matched actual conditions. DCA verified the model's clinical benefit.
Conclusion: Both TyG and UHR have good predictive value and are suitable for screening LEAD in T2DM patients.
期刊介绍：
An international, peer-reviewed, open access, online journal. The journal is committed to the rapid publication of the latest laboratory and clinical findings in the fields of diabetes, metabolic syndrome and obesity research. Original research, review, case reports, hypothesis formation, expert opinion and commentaries are all considered for publication.
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
